Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
Authors: Kai Liu, Haotong Qin, Yong Guo, Xin Yuan, Linghe Kong, Guihai Chen, Yulun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on different bits and scaling factors, the performance of DOBI can reach the state-of-the-art (SOTA) while after stage two, our method surpasses existing PTQ in both metrics and visual effects. |
| Researcher Affiliation | Academia | Kai Liu1, Haotong Qin2, Yong Guo3, Xin Yuan4, Linghe Kong1 , Guihai Chen1, Yulun Zhang1 1Shanghai Jiao Tong University, 2ETH Zürich, 3Max Planck Institute for Informatics, 4Westlake University |
| Pseudocode | Yes | Algorithm 1: DOBI pipeline |
| Open Source Code | Yes | The code and models are available at https://github.com/Kai-Liu001/2DQuant. |
| Open Datasets | Yes | We use DF2K [40, 34] as the training data, which combines DIV2K [40] and Flickr2K [34], as utilized by most SR models. |
| Dataset Splits | Yes | We use DF2K [40, 34] as the training data, which combines DIV2K [40] and Flickr2K [34], as utilized by most SR models. During training, since we employ a distillation training method, we do not need to use the high-resolution parts of the DF2K images. For validation, we use the Set5 [2] as the validation set. After selecting the best model, we tested it on five commonly used benchmarks in the SR field: Set5 [2], Set14 [45], B100 [36], Urban100 [18], and Manga109 [37]. |
| Hardware Specification | Yes | Our code is written with Python and Py Torch [38] and runs on an NVIDIA A800-80G GPU. |
| Software Dependencies | No | Our code is written with Python and Py Torch [38]. (No specific version numbers for Python or PyTorch are provided, nor for other libraries if used). |
| Experiment Setup | Yes | During DQC, we use the Adam [23] optimizer with a learning rate of 1 10 2, betas set to (0.9, 0.999), and a weight decay of 0. We employ Cosine Annealing [35] as the learning rate scheduler to stabilize the training process. Data augmentation is also performed. We randomly utilize rotation of 90 , 180 , and 270 and horizontal flips to augment the input image. The total iteration for training is 3,000 with batch size of 32. |